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A local AI model is one that runs its inference—the work of interpreting a prompt and generating a result—on your device or another computer on your local network, rather than sending the request to a remote AI service. The model may still need to be downloaded first, and the app around it may still use the internet.
What “local AI model” means
“Local” describes where inference runs, not how the model was built. A local model can be proprietary or open source, large or small, and downloaded from the internet before use. What makes it local is that the device or local computer processes the inference request instead of sending that request to a remote inference service.
For example, Apple’s Core AI documentation describes models that an app can bundle in its Xcode project or download for on-device inference. Apple says, “Inference happens on device, so data stays private, AI features can be readily available and work offline, and there is no per-inference cost to you or the people using your app.” That describes Apple’s documented implementation, not a guarantee about every app marketed as local AI. Apple Developer Documentation.
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It can, once the model and any required files are available on the device. Downloading the model, refreshing a catalog or metadata, and receiving software or model updates may still require internet access.
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- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
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Microsoft says Foundry Local requires an internet connection for the initial model download, but cached inference can continue offline. Its Windows FAQ, last updated July 15, 2026, says inputs and outputs for Foundry Local inference do not leave the machine. This is specific to that runtime and its inference path; it does not establish that every app component is offline. Microsoft’s Windows AI FAQ.
Does local AI keep your data private?
Running inference locally can reduce the amount of prompt data sent to an AI provider because the prompt does not need to travel to a remote inference service. But the label “local” is not a blanket privacy guarantee for the whole application. An app may still download files, refresh metadata, send diagnostics, or use a cloud service for some features.
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- Built for Local AI Development: AMD Ryzen AI Halo is designed for local AI development and inference, featuring 128GB unified memory and support for up to 200B parameter models to build and run intensive AI workloads locally.
- 128GB Unified Memory: Features 128GB LPDDR5x unified memory at 8000 MT/s with 256 GB/s memory bandwidth, providing a shared memory pool across the CPU, GPU, and NPU to support larger AI models.
- AMD Ryzen AI Max+ 395 Processor: Features 16 cores, 32 threads, and Zen 5 architecture, paired with AMD Radeon 8060S integrated graphics featuring 40 RDNA 3.5 compute units and an AMD XDNA 2 NPU with up to 50 TOPS.
- Linux AI Developer Platform: Purpose-built for Linux-based AI development with full AMD ROCm software support and preloaded tools, models, and workflows optimized for local AI development.
- Compact, Connected Design: Includes a 2TB M.2 SSD, 10GbE LAN, Wi-Fi 7, Bluetooth 5.4, USB-C connectivity, and HDMI 2.1b.
Check the app’s privacy documentation and network behavior, especially whether it offers cloud fallback. Microsoft notes that the owner of a local deployment remains responsible for security and updates. Microsoft’s local and cloud AI comparison, last updated September 21, 2026, discusses those responsibilities and trade-offs.
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What computer do you need to run AI locally?
There is no single minimum specification that applies to all local AI models. The requirements depend on the model, the task, the app, and the hardware available. Relevant resources include:
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- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
- Memory: The model and the app need enough memory to load and run the workload.
- Processor: Inference may use the CPU, GPU, or NPU, depending on the software and what the device supports.
- Storage: Model files take space on the device, in addition to the app and its other data.
- Model and task complexity: More demanding models or tasks may exceed what a particular device can handle well.
Microsoft’s Foundry Local runtime can select among available processors, but a device that supports one local model is not necessarily suitable for every other model. Copilot+ PCs include built-in AI features; that does not mean every Copilot+ PC can run every local model. Compare the requirements of the specific model and software you intend to use rather than relying on a universal RAM or model-size threshold. Microsoft’s comparison of local and cloud AI and its Foundry Local overview describe hardware selection and local inference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Local AI vs. cloud AI
| Consideration | Local inference | Cloud inference |
|---|---|---|
| Where processing runs | On your device or local computer. | On a remote service provider’s infrastructure. |
| Prompt data | The prompt need not be sent to a remote inference service, though other app components may connect online. | The request is sent to the service to be processed. |
| Hardware and model capability | Constrained by the available device resources and the model it can run. | Can draw on scalable resources and larger models. |
| Network dependence | Inference may continue offline after setup if the needed files are cached. | Usually depends on a network connection to reach the service. |
| Maintenance and scale | The local deployment owner is responsible for security and updates; capacity is tied to local devices. | The provider operates the service infrastructure, which can support use beyond one device’s capacity. |
These are general trade-offs, not a claim that one approach is always faster, cheaper, or more capable. Actual latency and operating costs depend on the device, service, workload, connectivity, and maintenance needs. Microsoft identifies model complexity, latency, connectivity, cost, maintenance, and scale as factors to weigh. Microsoft Learn.
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- 【Leading AI Mini Workstation】MINISFORUM AI MS-S1 Max Workstation comes with AMD Ryzen AI Max+ 395 processor, which uses AMD's latest generation Zen 5 architecture. It has 16 Cores and 32 Threads, the boost clock is up to 5.1GHz. The overall processor performance is up to 126 TOPS, and the NPU performance reaches up to 50 TOPS. AMD Ryzen AI enables improved productivity, advanced collaboration, and improved efficiency.
- 【AMD Radeon 8060S Graphics 】The MS-S1 Max Mini PC equipped with AMD Radeon 8060S Graphics which built on the new generation of RDNA 3.5 architecture AMD graphics, it brings ultra-high frame rate experiences and advanced content creation features anywhere and delivers staggering performance. It can handle all your computing and multimedia tasks efficiently.
- 【Five 8K Video Output】This MS-S1 Max Workstation comes with five video outputs, 1x HDMI (8K@60Hz), 2x USB4(40Gbps,Alt DP2.0,PD out 15W) and 2x USB4 V2(80Gbps,Alt DP2.0,PD out 15W) Outputs, which support multiple monitors display at the same time and provide a larger and wider filed of view and improve your work efficiency. It is used in fields that require high-performance computing and graphics processing, including digital signage and securities trading, as well as work that uses CAD, such as engineering design, scientific calculations, animation production, and post-production for movies and television
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When a hybrid local-and-cloud setup makes sense
A hybrid system can try local inference first and send a request to a cloud model when the local model is unavailable or not capable enough for the task. This can combine local processing for supported work with access to larger cloud models, but it also means some requests may leave the device.
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For a hybrid design, make the fallback conditions and data movement clear to users, and ensure the relevant consent and policy choices permit cloud processing. Microsoft recommends checking local feature readiness before invoking it and describes hybrid fallback in its local-versus-cloud guidance.
Quick Recap
What to check before choosing local AI
- Does the specific app run inference locally, or does it only offer some local features?
- Can you download the model and use it without a network connection afterward?
- Does the app send diagnostics, refresh metadata, or use cloud fallback?
- Does your device meet the requirements for the model and tasks you plan to use?
- Who handles security, model updates, and maintenance?
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